Automatic geosteering and evolutionary algorithm for use with same

ABSTRACT

A method, apparatus, and computer-readable medium provide automatic geosteering by automatically updating a geosteering structure model based upon observed data gathered during a drilling operation. In some embodiments, automatic updates may be used to introduce vertical shifts into a geosteering structure model to match synthetic log data with observed log data. In addition, in some embodiments an evolutionary algorithm may be used to introduce such vertical shifts and thereby provide an optimal match between the synthetic and observed log data.

BACKGROUND

Geosteering is a process used to manage the trajectory of a boreholebased on geological information gathered during a drilling operation,and a goal may be to reach specific geological targets. With somegeosteering operations, a well plan with an expected well path isdeveloped, and while a borehole is being drilled according to the wellplan, geological information is gathered so that the well plan can berevised as necessary to reach a desired geological target. Thegeological information may be gathered, for example, using mud logging,measurement while drilling (MWD), or logging while drilling (LWD).

Some conventional geosteering approaches rely on a geosteering structuremodel based on gamma ray (GR) or resistivity logs. In the case of a GRlog model, the model may be initialized with flat layers with constantGR values, e.g., based on measurements taken from a pilot well GR log.Then, during drilling of the horizontal part of a wellbore, real timeinformation, such as a measured GR log and real time formation telemetrydata measurements, are collected from the well drilling tool.

As drilling progresses, geosteering software allows an operator tovisualize differences between the structure model and the observed data,e.g., by comparing a synthetic GR log that is calculated from thestructure model and the known well trajectory and current position, anda measured GR log calculated from the real time measurements. Based uponthese differences, an operator may be permitted to manually modify thestructure model to better match the synthetic and measured GR logs.Accordingly, geosteering can be a highly involved process and can behighly dependent upon the expertise and experience of the operator.

SUMMARY

The embodiments disclosed herein provide a method, apparatus, andprogram product that provide automatic geosteering by automaticallyupdating a geosteering structure model based upon observed data gatheredduring a drilling operation. In some embodiments, automatic updates maybe used to introduce vertical shifts into a geosteering structure modelto match synthetic log data with observed log data. In addition, in someembodiments an evolutionary algorithm may be used to introduce suchvertical shifts and thereby provide an optimal match between thesynthetic and observed log data.

Therefore, consistent with an embodiment, automatic geosteering may beperformed by generating synthetic log data from a geosteering structuremodel, and using at least one processor, automatically updating thegeosteering structure model to match the synthetic log data withobserved log data collected during a drilling operation.

This summary is merely provided to introduce a selection of conceptsthat are further described below in the detailed description, and is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example hardware and softwareenvironment for a data processing system in accordance withimplementation of various technologies and techniques described herein.

FIGS. 2A-2D illustrate simplified, schematic views of an oilfield havingsubterranean formations containing reservoirs therein in accordance withimplementations of various technologies and techniques described herein.

FIG. 3 illustrates a schematic view, partially in cross section of anoilfield having a plurality of data acquisition tools positioned atvarious locations along the oilfield for collecting data from thesubterranean formations in accordance with implementations of varioustechnologies and techniques described herein.

FIG. 4 illustrates a production system for performing one or moreoilfield operations in accordance with implementations of varioustechnologies and techniques described herein.

FIG. 5 is a flowchart illustrating an example sequence of operationsused to perform automatic geosteering in the system of FIG. 1.

FIG. 6 is a block diagram illustrating crossover and mutation geneticoperations used in the sequence of operations of FIG. 5.

FIGS. 7 and 8 are graphs of an initial well section, projected welltrajectory and initial geosteering structure model based on pilot welldata and pilot well GR logs, respectively.

FIGS. 9-12 are graphs of automatic updates to the initial geosteeringstructure model and projected trajectory during intermediate (FIGS.9-11) and final (FIG. 12) stages of a drilling operation, and utilizingthe sequence of operations of FIG. 5.

DETAILED DESCRIPTION

Embodiments consistent with the present disclosure implement automaticgeosteering that automatically updates a geosteering structure modelbased upon observed or measurement data gathered during a drillingoperation. In some embodiments, an evolutionary algorithm is used toautomatically update the geosteering structure model to match observedand synthetic logs, e.g., observed and synthetic gamma ray (GR) orresistivity logs. Updates to the geosteering structure model then allowfor updates to be manually or automatically made to a vertical drillingangle used during a drilling operation in a horizontal well to fit welltrajectory with a target layer, or geological target.

Some embodiments attempt to identify vertical shifts of a geosteeringstructure model in a defined window proximate the bottom of a drilledhorizontal well to match a synthetic GR or resistivity log or curve toobserved GR or resistivity log or curve over this window. In someembodiments, an evolutionary algorithm may be used to identify verticalshifts in the defined window based on an iterative creation of sets(generation) of the separate solutions of the vertical shifts withselection of a solution based upon calculation for every solutionobjective function (fitness function), e.g., a minimum square functionto measure closeness of observed and synthetic GR logs. Also, in someembodiments, a fitness function such as a sum of square vertical shiftsmay be used to find a smoothed version of the solution. Also, in someembodiments horizontal variance of the vertical shifts may be used forevolutionary matching because in some instances it may be more naturalto find parameters close to structure angles.

Other variations and modifications will be apparent to one of ordinaryskill in the art.

Hardware and Software Environment

Turning now to the drawings, FIG. 1 illustrates an example dataprocessing system 10 in which the various technologies and techniquesdescribed herein may be implemented. System 10 is illustrated as amodeling system including one or more computers, each including acentral processing unit 12 including at least one hardware-basedmicroprocessor coupled to a memory 14, which may represent the randomaccess memory (RAM) devices comprising the main storage of a computer,as well as any supplemental levels of memory, e.g., cache memories,non-volatile or backup memories (e.g., programmable or flash memories),read-only memories, etc. In addition, memory 14 may be considered toinclude memory storage physically located elsewhere in a computer, e.g.,any cache memory in a microprocessor, as well as any storage capacityused as a virtual memory, e.g., as stored on a mass storage device 16 oron another computer or networked storage device. System 10 may beincorporated into a clustering environment and implemented on aplurality of computer nodes, or may reside in one or more virtualmachines executing in one or more physical machines.

System 10 also receives a number of inputs and outputs for communicatinginformation externally. For interface with a user or operator, system 10includes a user interface 18 incorporating one or more user inputdevices, e.g., a keyboard, a pointing device, a display, a printer, etc.Otherwise, user input may be received, e.g., over a network interface 20coupled to a network 22, from one or more client computers 24, as wellas from one or more wellsites 26, each of which may include one or moresensors 28. System 10 also may be in communication with one or more massstorage devices 16, which may be, for example, internal hard diskstorage devices, external hard disk storage devices, storage areanetwork devices, etc.

The system 10 operates under the control of an operating system 30 andexecutes or otherwise relies upon various computer softwareapplications, components, programs, objects, modules, data structures,etc. For example, a petro-technical modeling platform 32 may include ageosteering application 34, and rely on a database 36 within which isstored information including modeling data 38 and observed data 40.

In general, the routines executed to implement the embodiments disclosedherein, whether implemented as part of an operating system or a specificapplication, component, program, object, module or sequence ofinstructions, or even a subset thereof, will be referred to herein as“computer program code,” or simply “program code.” Program codecomprises one or more instructions that are resident at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processors in a computer, cause thatcomputer to perform the steps necessary to execute steps or elementsembodying desired functionality. Moreover, while embodiments have andhereinafter will be described in the context of fully functioningcomputers and computer systems, those skilled in the art will appreciatethat the various embodiments are capable of being distributed as aprogram product in a variety of forms, and that the invention appliesequally regardless of the particular type of computer readable mediaused to actually carry out the distribution.

Such computer readable media may include computer readable storage mediaand communication media. Computer readable storage media isnon-transitory in nature, and may include volatile and non-volatile, andremovable and non-removable media implemented in any method ortechnology for storage of information, such as computer-readableinstructions, data structures, program modules or other data. Computerreadable storage media may further include RAM, ROM, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), flash memory or other solidstate memory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store thedesired information and which can be accessed by computer 10.Communication media may embody computer readable instructions, datastructures or other program modules. By way of example, and notlimitation, communication media may include wired media such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media. Combinations of any of the abovemay also be included within the scope of computer readable media.

Various program code described hereinafter may be identified based uponthe application within which it is implemented in a specific embodimentof the invention. However, it should be appreciated that any particularprogram nomenclature that follows is used merely for convenience, andthus the invention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature. Furthermore,given the endless number of manners in which computer programs may beorganized into routines, procedures, methods, modules, objects, and thelike, as well as the various manners in which program functionality maybe allocated among various software layers that are resident within atypical computer (e.g., operating systems, libraries, API's,applications, applets, etc.), it should be appreciated that theinvention is not limited to the specific organization and allocation ofprogram functionality described herein.

Those skilled in the art will recognize that the example environmentillustrated in FIG. 1 is not intended to limit the invention. Indeed,those skilled in the art will recognize that other alternative hardwareand/or software environments may be used without departing from thescope of the invention.

Oilfield Operations

FIGS. 2A-2D illustrate simplified, schematic views of an oilfield 100having subterranean formation 102 containing reservoir 104 therein inaccordance with implementations of various technologies and techniquesdescribed herein. FIG. 2A illustrates a survey operation being performedby a survey tool, such as seismic truck 106.1, to measure properties ofthe subterranean formation. The survey operation is a seismic surveyoperation for producing sound vibrations. In FIG. 2A, one such soundvibration, sound vibration 112 generated by source 110, reflects offhorizons 114 in earth formation 116. A set of sound vibrations isreceived by sensors, such as geophone-receivers 118, situated on theearth's surface. The data received 120 is provided as input data to acomputer 122.1 of a seismic truck 106.1, and responsive to the inputdata, computer 122.1 generates seismic data output 124. This seismicdata output may be stored, transmitted or further processed as desired,for example, by data reduction.

FIG. 2B illustrates a drilling operation being performed by drillingtools 106.2 suspended by rig 128 and advanced into subterraneanformations 102 to form wellbore 136. Mud pit 130 is used to drawdrilling mud into the drilling tools via flow line 132 for circulatingdrilling mud down through the drilling tools, then up wellbore 136 andback to the surface. The drilling mud is usually filtered and returnedto the mud pit. A circulating system may be used for storing,controlling, or filtering the flowing drilling muds. The drilling toolsare advanced into subterranean formations 102 to reach reservoir 104.Each well may target one or more reservoirs. The drilling tools areadapted for measuring downhole properties using logging while drillingtools. The logging while drilling tools may also be adapted for takingcore sample 133 as shown.

Computer facilities may be positioned at various locations about theoilfield 100 (e.g., the surface unit 134) and/or at remote locations.Surface unit 134 may be used to communicate with the drilling toolsand/or offsite operations, as well as with other surface or downholesensors. Surface unit 134 is capable of communicating with the drillingtools to send commands to the drilling tools, and to receive datatherefrom. Surface unit 134 may also collect data generated during thedrilling operation and produces data output 135, which may then bestored or transmitted.

Sensors (S), such as gauges, may be positioned about oilfield 100 tocollect data relating to various oilfield operations as describedpreviously. As shown, sensor (S) is positioned in one or more locationsin the drilling tools and/or at rig 128 to measure drilling parameters,such as weight on bit, torque on bit, pressures, temperatures, flowrates, compositions, rotary speed, and/or other parameters of the fieldoperation. Sensors (S) may also be positioned in one or more locationsin the circulating system.

Drilling tools 106.2 may include a bottom hole assembly (BHA) (notshown), generally referenced, near the drill bit (e.g., within severaldrill collar lengths from the drill bit). The bottom hole assemblyincludes capabilities for measuring, processing, and storinginformation, as well as communicating with surface unit 134. The bottomhole assembly further includes drill collars for performing variousother measurement functions.

The bottom hole assembly may include a communication subassembly thatcommunicates with surface unit 134. The communication subassembly isadapted to send signals to and receive signals from the surface using acommunications channel such as mud pulse telemetry, electro-magnetictelemetry, or wired drill pipe communications. The communicationsubassembly may include, for example, a transmitter that generates asignal, such as an acoustic or electromagnetic signal, which isrepresentative of the measured drilling parameters. It will beappreciated by one of skill in the art that a variety of telemetrysystems may be employed, such as wired drill pipe, electromagnetic orother known telemetry systems.

Generally, the wellbore is drilled according to a drilling plan that isestablished prior to drilling. The drilling plan sets forth equipment,pressures, trajectories and/or other parameters that define the drillingprocess for the wellsite. The drilling operation may then be performedaccording to the drilling plan. However, as information is gathered, thedrilling operation may need to deviate from the drilling plan.Additionally, as drilling or other operations are performed, thesubsurface conditions may change. The earth model may also needadjustment as new information is collected

The data gathered by sensors (S) may be collected by surface unit 134and/or other data collection sources for analysis or other processing.The data collected by sensors (S) may be used alone or in combinationwith other data. The data may be collected in one or more databasesand/or transmitted on or offsite. The data may be historical data, realtime data, or combinations thereof. The real time data may be used inreal time, or stored for later use. The data may also be combined withhistorical data or other inputs for further analysis. The data may bestored in separate databases, or combined into a single database.

Surface unit 134 may include transceiver 137 to allow communicationsbetween surface unit 134 and various portions of the oilfield 100 orother locations. Surface unit 134 may also be provided with orfunctionally connected to one or more controllers (not shown) foractuating mechanisms at oilfield 100. Surface unit 134 may then sendcommand signals to oilfield 100 in response to data received. Surfaceunit 134 may receive commands via transceiver 137 or may itself executecommands to the controller. A processor may be provided to analyze thedata (locally or remotely), make the decisions and/or actuate thecontroller. In this manner, oilfield 100 may be selectively adjustedbased on the data collected. This technique may be used to optimizeportions of the field operation, such as controlling drilling, weight onbit, pump rates, or other parameters. These adjustments may be madeautomatically based on computer protocol, and/or manually by anoperator. In some cases, well plans may be adjusted to select optimumoperating conditions, or to avoid problems.

FIG. 2C illustrates a wireline operation being performed by wirelinetool 106.3 suspended by rig 128 and into wellbore 136 of FIG. 2B.Wireline tool 106.3 is adapted for deployment into wellbore 136 forgenerating well logs, performing downhole tests and/or collectingsamples. Wireline tool 106.3 may be used to provide another method andapparatus for performing a seismic survey operation. Wireline tool 106.3may, for example, have an explosive, radioactive, electrical, oracoustic energy source 144 that sends and/or receives electrical signalsto surrounding subterranean formations 102 and fluids therein.

Wireline tool 106.3 may be operatively connected to, for example,geophones 118 and a computer 122.1 of a seismic truck 106.1 of FIG. 2A.Wireline tool 106.3 may also provide data to surface unit 134. Surfaceunit 134 may collect data generated during the wireline operation andmay produce data output 135 that may be stored or transmitted. Wirelinetool 106.3 may be positioned at various depths in the wellbore 136 toprovide a survey or other information relating to the subterraneanformation 102.

Sensors (S), such as gauges, may be positioned about oilfield 100 tocollect data relating to various field operations as describedpreviously. As shown, sensor S is positioned in wireline tool 106.3 tomeasure downhole parameters which relate to, for example porosity,permeability, fluid composition and/or other parameters of the fieldoperation.

FIG. 2D illustrates a production operation being performed by productiontool 106.4 deployed from a production unit or Christmas tree 129 andinto completed wellbore 136 for drawing fluid from the downholereservoirs into surface facilities 142. The fluid flows from reservoir104 through perforations in the casing (not shown) and into productiontool 106.4 in wellbore 136 and to surface facilities 142 via gatheringnetwork 146.

Sensors (S), such as gauges, may be positioned about oilfield 100 tocollect data relating to various field operations as describedpreviously. As shown, the sensor (S) may be positioned in productiontool 106.4 or associated equipment, such as Christmas tree 129,gathering network 146, surface facility 142, and/or the productionfacility, to measure fluid parameters, such as fluid composition, flowrates, pressures, temperatures, and/or other parameters of theproduction operation.

Production may also include injection wells for added recovery. One ormore gathering facilities may be operatively connected to one or more ofthe wellsites for selectively collecting downhole fluids from thewellsite(s).

While FIGS. 2B-2D illustrate tools used to measure properties of anoilfield, it will be appreciated that the tools may be used inconnection with non-oilfield operations, such as gas fields, mines,aquifers, storage, or other subterranean facilities. Also, while certaindata acquisition tools are depicted, it will be appreciated that variousmeasurement tools capable of sensing parameters, such as seismic two-waytravel time, density, resistivity, production rate, etc., of thesubterranean formation and/or its geological formations may be used.Various sensors (S) may be located at various positions along thewellbore and/or the monitoring tools to collect and/or monitor thedesired data. Other sources of data may also be provided from offsitelocations.

The field configurations of FIGS. 2A-2D are intended to provide a briefdescription of an example of a field usable with oilfield applicationframeworks. Part, or all, of oilfield 100 may be on land, water, and/orsea. Also, while a single field measured at a single location isdepicted, oilfield applications may be utilized with any combination ofone or more oilfields, one or more processing facilities and one or morewellsites.

FIG. 3 illustrates a schematic view, partially in cross section ofoilfield 200 having data acquisition tools 202.1, 202.2, 202.3 and 202.4positioned at various locations along oilfield 200 for collecting dataof subterranean formation 204 in accordance with implementations ofvarious technologies and techniques described herein. Data acquisitiontools 202.1-202.4 may be the same as data acquisition tools 106.1-106.4of FIGS. 2A-2D, respectively, or others not depicted. As shown, dataacquisition tools 202.1-202.4 generate data plots or measurements208.1-208.4, respectively. These data plots are depicted along oilfield200 to demonstrate the data generated by the various operations.

Data plots 208.1-208.3 are examples of static data plots that may begenerated by data acquisition tools 202.1-202.3, respectively, however,it should be understood that data plots 208.1-208.3 may also be dataplots that are updated in real time. These measurements may be analyzedto better define the properties of the formation(s) and/or determine theaccuracy of the measurements and/or for checking for errors. The plotsof each of the respective measurements may be aligned and scaled forcomparison and verification of the properties.

Static data plot 208.1 is a seismic two-way response over a period oftime. Static plot 208.2 is core sample data measured from a core sampleof the formation 204. The core sample may be used to provide data, suchas a graph of the density, porosity, permeability, or some otherphysical property of the core sample over the length of the core. Testsfor density and viscosity may be performed on the fluids in the core atvarying pressures and temperatures. Static data plot 208.3 is a loggingtrace that provides a resistivity or other measurement of the formationat various depths.

A production decline curve or graph 208.4 is a dynamic data plot of thefluid flow rate over time. The production decline curve provides theproduction rate as a function of time. As the fluid flows through thewellbore, measurements are taken of fluid properties, such as flowrates, pressures, composition, etc.

Other data may also be collected, such as historical data, user inputs,economic information, and/or other measurement data and other parametersof interest. As described below, the static and dynamic measurements maybe analyzed and used to generate models of the subterranean formation todetermine characteristics thereof Similar measurements may also be usedto measure changes in formation aspects over time.

The subterranean structure 204 has a plurality of geological formations206.1-206.4. As shown, this structure has several formations or layers,including a shale layer 206.1, a carbonate layer 206.2, a shale layer206.3 and a sand layer 206.4. A fault 207 extends through the shalelayer 206.1 and the carbonate layer 206.2. The static data acquisitiontools are adapted to take measurements and detect characteristics of theformations.

While a specific subterranean formation with specific geologicalstructures is depicted, it will be appreciated that oilfield 200 maycontain a variety of geological structures and/or formations, sometimeshaving extreme complexity. In some locations, e.g., below the waterline, fluid may occupy pore spaces of the formations. Each of themeasurement devices may be used to measure properties of the formationsand/or its geological features. While each acquisition tool is shown asbeing in specific locations in oilfield 200, it will be appreciated thatone or more types of measurement may be taken at one or more locationsacross one or more fields or other locations for comparison and/oranalysis.

The data collected from various sources, such as the data acquisitiontools of FIG. 3, may then be processed and/or evaluated. Generally,seismic data displayed in static data plot 208.1 from data acquisitiontool 202.1 may be used by a geophysicist to determine characteristics ofthe subterranean formations and features. The core data shown in staticplot 208.2 and/or log data from well log 208.3 are used by a geologistto determine various characteristics of the subterranean formation. Theproduction data from graph 208.4 is used by the reservoir engineer todetermine fluid flow reservoir characteristics. The data analyzed by thegeologist, geophysicist and the reservoir engineer may be analyzed usingmodeling techniques.

FIG. 4 illustrates an oilfield 300 for performing production operationsin accordance with implementations of various technologies andtechniques described herein. As shown, the oilfield has a plurality ofwellsites 302 operatively connected to central processing facility 354.The oilfield configuration of FIG. 4 is not intended to limit the scopeof the oilfield application system. Part of or the entire oilfield maybe on land and/or sea. Also, while a single oilfield with a singleprocessing facility and a plurality of wellsites is depicted, anycombination of one or more oilfields, one or more processing facilitiesand one or more wellsites may be present.

Each wellsite 302 has equipment that forms wellbore 336 into the earth.The wellbores 336 extend through subterranean formations 306 includingreservoirs 304. These reservoirs 304 contain fluids, such ashydrocarbons. It will be appreciated that horizontal drilling may beused for some of the wellbores, e.g., as shown by horizontal wellbore338 in FIG. 4. The wellsites draw fluid from the reservoirs and passthem to the processing facilities via surface networks 344. The surfacenetworks 344 have tubing and control mechanisms for controlling the flowof fluids from the wellsite to processing facility 354.

Automatic Geosteering

Geosteering is a process used to manage the trajectory of a boreholebased on geological information gathered during a drilling operation,and a goal may include reaching specific geological targets. Accordingto an embodiment, geosteering software used for this process may haveone or more of the following characteristics: (1) visualization is basedon a two dimensional (2D) cross section along a well trajectoryprojection to surface; (2) gamma ray (GR) logs are used; (3) ageosteering structure and synthetic GR model may be created with initiallayers (e.g., flat layers) having constant GR values based on a pilotwell GR log; (4) during drilling of a horizontal part of a wellbore,real time information about the trajectory and measured GR log, e.g.,real time formation telemetry data measurements, may be obtained from awell drilling tool; (5) a synthetic GR log is calculated via the welltrajectory and current position of the GR model; (6) the geosteeringsoftware may allow an operator to interactively and/or manually changethe dip of the structure model to match the measured and synthetic logs;(7) the geosteering software may allow the operator to perform matchingusing vertical shifts of the structure model in a defined position tomodel structural faults; and (8) the geosteering software may allow anoperator to visually match the observed and synthetic logs on 2D crosssections. With respect to (3), besides flat initial layers, other shapesor configurations of the layers can be used, such as a dipped layerstructure using log data from one or two offset wells. In an exampleembodiment, the system can also create the initial model using regionalstructure surfaces, when they are available. An example advantage of anexample embodiment that uses regional structure surfaces may be that theinitial model will be closer to the actual structure than models withflat layers

Visually matching observed and synthetic logs, and manually changing ageosteering structure model can involve the efforts of an experiencedoperator. Embodiments consistent with the present disclosure, may employan automatic geosteering process to automate, guide, and/or otherwisemanage the updating of a geosteering structure model based upon observeddata generated in the course of a drilling operation. A geosteeringstructure model may be dynamically updated based upon an algorithm thatmatches a synthetic log, e.g., a synthetic GR log, to a correspondingobserved log, e.g., an observed GR log as new data is collected. In someembodiments, an evolutionary algorithm may be used for matching over thenew data. In addition, as will become more apparent below, a morecontinuous result may be obtained in some embodiments by matchingprevious potions of data according predefined parameters.

Returning to FIG. 1, a geosteering application 34 incorporating theherein-described functionality may be implemented within apetro-technical modeling platform 32, or may be implemented separatelytherefrom. A geosteering structure model and associated synthetic logs(e.g., GR, resistivity or other logs) may be defined and stored asmodeling data 38 along with observed logs, telemetry data and otherobserved data 40 in a database 36 that is either integrated into system10 or accessible thereto over a network. The observed data 40 may becollected from one or more sensors 28 at one or more wellsites, e.g.,sensors used for mud logging, measurement while drilling (MWD) orlogging while drilling (LWD). According to an embodiment, an updatedwell plan, revised based upon an updated geosteering structure model,may be communicated back to an appropriate well site and used to controlthe trajectory of a drilling operation.

FIG. 5 illustrates at 400 an example of operations for an evolutionaryalgorithm implemented by, accessed by, and/or interfaced to geosteeringapplication 34 to perform automated updates to a geosteering structuremodel to create a better match or correspondence between synthetic andobserved logs, which may include GR logs. In an example embodiment,multiple iterations can be run based on different scenarios to find aselected, threshold, best, and/or most closely-matched fit betweensynthetic and observed logs using one or more evolutionary or geneticoperations, such as crossover and/or mutation operations. Anevolutionary operation or process can, in some embodiments, be appliedfor each of a series of iterations or generations.

Starting in block 402, a plurality of sets of initial shiftcombinations, or sets of shifts, for the geosteering structural modelcan be generated. In embodiments, the shifts can be or include a set ofvertical shifts of the geological structures represented by the model.In embodiments, the shifts can be based on a random function within amaximum possible shift, thereby creating an initial generation ofcandidate solutions. In addition, in some embodiments, a range ofhorizontal variance of vertical shifts can be used in addition to orinstead of the vertical shifts themselves, since it has been observedthat the use of horizontal variances can be useful in identifyingparameters close to structure angles, under some circumstances. It willbe appreciated that while the use of absolute vertical shifts and/orvariances in vertical shifts are noted, other values, including otherperturbations or variations in the underlying geological structures ofthe model, can be used.

In block 404, a synthetic GR curve can be calculated for every set ofvertical shifts, as well as a corresponding fitness function that isused to “score” the associated set of shifts. For example, a fitnessfunction such as a minimum or least-squares function, and/or otherobjective function or measure, may be used to quantify or measure thecloseness, correspondence, degree of matching, and/or similarity of theobserved and synthetic GR logs together with a square function of thevertical shifts. In some embodiments, an additional function may beadded to the fitness function with a predefined coefficient alpha togenerate smoothed variants of the vertical shifts. While the generationof a synthetic GR curve is noted, it will likewise be appreciated thatother curves, graphs, and/or types of probes or signals can be used,such as resistivity curves, logs, or functions.

In block 406, selection of candidate solutions for the next evolutionarygeneration can be selected or identified by selecting a subset of thesets of shifts to the geosteering structural model which produce thebest, closest, and/or otherwise qualified or selected matches orcorrespondence, based upon the fitness function scores and/or metrics.

In block 408, one or more evolutionary algorithms or operations, e.g.,genetic operations, can be performed to “breed” or evolve the candidatesolutions. Those evolutionary (or genetic) algorithms can be or includecrossover and/or mutation operations. For instance, as shown in FIG. 6,a crossover of shifts 450, 452 between parents A/B and children A′/B′may be performed to exchange shift values between sets of shifts.Analogizing a set of shifts as a chromosome and a shift within a set ofshifts as a gene on a chromosome, a crossover operation can exchangegenes (i.e., shifts) between chromosomes (i.e., groups or sets ofshifts). The number of genes exchanged may be singular or multiple, andmay occur with a given or differing probability within and/or after eachiteration.

In addition, as also shown by shifts 454 in FIG. 6, a mutation operationmay also be performed, in addition to or instead of a crossoveroperation. As with biological evolution theory, genes can replacedrandomly within chromosomes in successive generations. The effects ofthose random substitutions can in general include a reduction of thelikelihood that the process will converge to a local minimum. This canbe advantageous because an evolutionary history which has converged to alocal minimum may become fixed or “stuck” at that value for the subjectgene/chromosome value, while a better global minimum value may existoutside of that value in the gene/chromosome.

The probability of occurrence of a mutation, crossover, and/or othergenetic or other evolutionary can be configured to be a function of theiteration (or generation) step itself Thus, for instance, a mutation canbe configured to be more likely to happen as soon as the evolution ofthe fitness function is reaching a plateau, minimum, and/or other point,event, or condition. In some embodiments, the mutation probability maybe configured to be lower (e.g., significantly lower) than thecross-over probability, but other relationships between the likelihoodof mutation and cross-over can be used.

While mutation and cross-over events are described, otherevolutionary/genetic operations or processes, and/or complementary oradditional algorithms or processing, can be used. This can includeevents, algorithms, and/or processes such as re-grouping,colonization-extinction, migration, and others. For instance, Gaussianadaptation, simulated annealing, diversity algorithms or techniques,and/or other algorithms or techniques can also be used to adjust theresults of the genetic operations themselves.

In an example embodiment, it may also be desirable in block 408 tomaintain the population of candidate solutions (sets ofshifts/variances) relatively constant from generation to generation(e.g., at 50 candidates). In such an embodiment, even as selection isreducing the number of the population (e.g., by taking the 10 candidateswith the best fitness function scores), applying crossover and mutationto those selected candidates may be used to regenerate a full set of 50“chromosomes” in the population, which may in cases avoid convergence toa local minimum or other best outcome which does not represent theglobal minimum or other best outcome.

Block 410 determines whether a termination condition is met, e.g.,whether a selected or predetermined (N) iterations have occurred,whether a candidate solution has produced a fitness function above orbelow a predetermined threshold, or some other measure, condition,and/or indicator that the evolutionary processing has converged on asuitable solution. If not, control returns to block 404 to process a newgeneration of variations and solutions. Otherwise, control passes toblock 412 to select an optimal or best-available set ofshifts/variances, and/or otherwise select a set of shifts/variances, andupdate the geosteering structure model accordingly. Block 414 can thenupdate the associated well plan based on the updated geological modelupon which the geosteering operations are based.

In block 416, as long as the drilling operation is not yet complete,control passes to block 418 to wait for additional observed data to beobtained (e.g., after further drilling has been performed, and new GRand/or other log data has been retrieved). Once new data is obtained,control returns to block 402 to perform the various evolutionaryalgorithms and techniques again, based on the newly acquired or accesseddata. Once the overall drilling operation is complete or at other times,the sequence of operations 400 can be rendered complete.

As a representative example, FIGS. 7 and 8 illustrate an initial wellsection with a projected well trajectory and initial geosteeringstructure model that is initially created with simple horizontal layersbased on pilot well data. FIG. 7, in particular, illustrates a wellsection and synthetic log (upper curve) with a projected horizontal welland an initial structure of the layers as created based on pilot welldata. FIG. 8 illustrates a well section and synthetic log (upper curve)with a projected horizontal well and GR model based on a pilot well GRlog.

FIGS. 9, 10, 11, and 12 illustrate the well section at different pointsin a drilling operation based upon gathered real time data, and thechanges to the geosteering structure model as a result of theherein-described evolutionary algorithms and techniques to obtaindesired or selected matching between the synthetic and observed GR logs.The upper horizontal track in each figure illustrates the degree ofcorrespondence between the synthetic and observed GR logs.

It may be noted that any intermediate results (FIGS. 9-11), or the finalresult (FIG. 12), can in example embodiments be manually edited toexplore or obtain better or different correspondence between thesynthetic and observed data logs. During manual editing, it may also bepossible to smooth the geological structure, change the structure angle,add vertical or non-vertical faults or blocks of faults, and/or provideother updates to better match the observed results with the geologicalmodel.

While illustrated embodiments utilize GR logs, it will again beappreciated that other embodiments may use resistivity or other data,signal, probes, or logs, such as optical, acoustical, and other data.Also, embodiments can be based on or utilize multiple logs (i.e.,multi-logs) captured over different positions, at certain times (e.g.,different times), using certain types of observed data signals (e.g.,different types), and/or data logs otherwise encoding multiple datasets.

While various particular embodiments have been described, it will beunderstood that the invention is not intended to be limited thereto. Inaddition, it will be appreciated that implementation of one or moreaspects of the aforementioned functionality, techniques, algorithms, andprocesses in software and thusly in a computer system executing suchsoftware would be within the abilities of one of ordinary skill, in theart having the benefit of the instant disclosure.

Yet other modifications could accordingly be made without deviating fromits spirit and scope as claimed.

What is claimed is:
 1. A method of performing geosteering, comprising:accessing synthetic log data based on a model of a geological formation;accessing observed log data collected from a drilling operation in thegeological formation; generating a plurality of updated models using anevolutionary algorithm; generating updated synthetic log data using atleast one the updated models; and selecting an updated model from theplurality of updated models based on a comparison of the observed logdata and the updated synthetic log data.
 2. The method of claim 1,wherein the evolutionary algorithm comprises performing at least one ofa crossover operation or a mutation operation.
 3. The method of claim 2,wherein generating the plurality of updated models comprises updatingthe model using at least one of a vertical shift, or a variance of avertical shift, of the model of the geological formation to perform theat least one of the crossover operation or the mutation operation. 4.The method of claim 1, wherein the selecting an updated model comprisesselecting an updated model for which the comparison produces a fitnessfunction measure between the observed data log and the updated syntheticlog data.
 5. The method of claim 1, wherein the synthetic log datacomprises at least one of: a synthetic gamma ray (GR) log, or asynthetic resistivity log.
 6. The method of claim 1, wherein theobserved log data comprises at least one of: an observed gamma ray (GR)log, or an observed resistivity log.
 7. The method of claim 1, furthercomprising repeating the generating a set of updated models using anevolutionary algorithm for a plurality of generations of updated modelseach based on an updated data log.
 8. A system, comprising: one or moreprocessors; and a memory comprising one or more computer-readable mediastoring synthetic log data based on a model of a geological formation,observed log data collected from a drilling operation in the geologicalformation, and instructions that, when executed by at least one of theone or more processors, cause the system to perform operations, theoperations comprising: generating a plurality of updated models using anevolutionary algorithm; generating updated synthetic log data using atleast one of the plurality of updated models; and selecting an updatedmodel from the plurality of updated models based on a comparison of theobserved log data, and the updated synthetic log data.
 9. The system ofclaim 8, wherein generating the plurality of updated models using theevolutionary algorithm comprises performing at least one of a crossoveroperation or a mutation operation.
 10. The system of claim 9, whereingenerating the plurality of updated models using the evolutionaryalgorithm comprises updating the model using at least one of a verticalshift, or a variance of a vertical shift, of the model of the geologicalformation to perform the at least one of the crossover operation or themutation operation.
 11. The system of claim 8, wherein selecting theupdated model comprises selecting the updated model for which thecomparison produces a fitness function measure between the observed datalog and the updated synthetic log data.
 12. The system of claim 8,wherein the synthetic log data comprises at least one of: a syntheticgamma ray (GR) log, or a synthetic resistivity log.
 13. The system ofclaim 8, wherein the observed log data comprises at least one of: anobserved gamma ray (GR) log, or an observed resistivity log.
 14. Thesystem of claim 8, wherein the operations further comprise repeatinggenerating the plurality of updated models using the evolutionaryalgorithm for a plurality of generations of updated models each based onan updated data log.
 15. A computer-readable medium storing instructionsthat, when executed, cause a processor system to perform operations, theoperations comprising: accessing synthetic log data based on a model ofa geological formation; accessing observed log data collected from adrilling operation in the geological formation; generating a pluralityof updated models using an evolutionary algorithm; generating updatedsynthetic log data using at least one of the plurality of updatedmodels; and selecting an updated model from the plurality of updatedmodels based on a comparison of the observed log data and the updatedsynthetic log data.
 16. The medium of claim 15, wherein generating theplurality of updated models using the evolutionary algorithm comprisesperforming at least one of a crossover operation or a mutationoperation.
 17. The medium of claim 16, wherein generating the pluralityof updated models comprises updating the model using at least one of avertical shift, or a variances of a vertical shift, of the model of thegeological formation to perform the at least one of the crossoveroperation or the mutation operation.
 18. The medium of claim 15, whereinthe selecting an updated model comprises selecting an updated model forwhich the comparison produces a fitness function measure between theobserved data log and each of the updated synthetic log data.
 19. Themedium of claim 15, wherein: the synthetic log data comprises at leastone of a synthetic gamma ray (GR) log, or a synthetic resistivity log;and the observed log data comprises at least one of an observed gammaray (GR) log, or an observed resistivity log.
 20. The medium of claim15, wherein the operations further comprise repeating the generating aset of updated models using an evolutionary algorithm for a plurality ofgenerations of updated models each based on an updated data log.
 21. Themethod of any of claims 1-7, wherein the evolutionary algorithmcomprises performing at least one of a crossover operation or a mutationoperation.
 22. The method of any of claims 1-7 and 21, whereingenerating the plurality of updated models comprises updating the modelusing at least one of a vertical shift, or a variance of a verticalshift, of the model of the geological formation to perform the at leastone of the crossover operation or the mutation operation.
 23. The methodof any of claims 1-7, 21, and 22, wherein the selecting an updated modelcomprises selecting an updated model for which the comparison produces afitness function measure between the observed data log and the updatedsynthetic log data.
 24. The method of any of claims 1-7 and 21-23,wherein the synthetic log data comprises at least one of: a syntheticgamma ray (GR) log, or a synthetic resistivity log.
 25. The method ofclaims 1-7 and 21-24, wherein the observed log data comprises at leastone of: an observed gamma ray (GR) log, or an observed resistivity log.26. The method of claims 1-7 and 21-25, further comprising repeating thegenerating a set of updated models using an evolutionary algorithm for aplurality of generations of updated models each based on an updated datalog.
 27. The system of any of claims 8-14, wherein generating theplurality of updated models using the evolutionary algorithm comprisesperforming at least one of a crossover operation or a mutationoperation.
 28. The system of any of claims 8-14 and 27, whereingenerating the plurality of updated models using the evolutionaryalgorithm comprises updating the model using at least one of a verticalshift, or a variance of a vertical shift, of the model of the geologicalformation to perform the at least one of the crossover operation or themutation operation.
 29. The system of any of claims 8-14, 27, and 28,wherein selecting the updated model comprises selecting the updatedmodel for which the comparison produces a fitness function measurebetween the observed data log and the updated synthetic log data. 30.The system of any of claims 8-14 and 27-29, wherein the synthetic logdata comprises at least one of: a synthetic gamma ray (GR) log, or asynthetic resistivity log.
 31. The system of any of claims 8-14 and27-30, wherein the observed log data comprises at least one of: anobserved gamma ray (GR) log, or an observed resistivity log.
 32. Thesystem of any of claims 8-14 and 27-31, wherein the operations furthercomprise repeating generating the plurality of updated models using theevolutionary algorithm for a plurality of generations of updated modelseach based on an updated data log.
 33. The medium of any of claims15-20, wherein generating the plurality of updated models using theevolutionary algorithm comprises performing at least one of a crossoveroperation or a mutation operation.
 34. The medium of any of claims 15-20and 33, wherein generating the plurality of updated models comprisesupdating the model using at least one of a vertical shift, or avariances of a vertical shift, of the model of the geological formationto perform the at least one of the crossover operation or the mutationoperation.
 35. The medium of any of claims 15-20, 33, and 34, whereinthe selecting an updated model comprises selecting an updated model forwhich the comparison produces a fitness function measure between theobserved data log and each of the updated synthetic log data.
 36. Themedium of any of claims 15-20 and 33-35, wherein: the synthetic log datacomprises at least one of a synthetic gamma ray (GR) log, or a syntheticresistivity log; and the observed log data comprises at least one of anobserved gamma ray (GR) log, or an observed resistivity log.
 37. Themedium any of claims 15-20 and 33-36, wherein the operations furthercomprise repeating the generating a set of updated models using anevolutionary algorithm for a plurality of generations of updated modelseach based on an updated data log.
 38. A computer-readable mediumstoring instructions that, when executed, cause a processor to perform amethod according to any of claims 1-8 and 21-26.